Brain-GCN-Net: Graph-Convolutional Neural Network for brain tumor identification.

BACKGROUND: The intersection of artificial intelligence and medical image analysis has ushered in a new era of innovation and changed the landscape of brain tumor detection and diagnosis. Correct detection and classification of brain tumors based on medical images is crucial for early diagnosis and effective treatment. Convolutional Neural Network (CNN) models are widely used for disease detection. However, they are sometimes unable to sufficiently recognize the complex features of medical images. METHODS: This paper proposes a fused Deep Learning (DL) model that combines Graph Neural Networks (GNN), which recognize relational dependencies of image regions, and CNN, which captures spatial features, is proposed to improve brain tumor detection. By integrating these two architectures, our model achieves a more comprehensive representation of brain tumor images and improves classification performance. The proposed model is evaluated on a public dataset of 10847 MRI images. The results show that the proposed model outperforms the existing pre-trained models and traditional CNN architectures. RESULTS: The fused DL model achieves 93.68% accuracy in brain tumor classification. The results indicate that the proposed model outperforms the existing pre-trained models and traditional CNN architectures. CONCLUSION: The numerical results suggest that the model should be further investigated for potential use in clinical trials to improve clinical decision-making.

Two reports of iatrogenic pseudoaneurysms intraoperatively induced by an ultrasonic surgical aspirator: A rare intraoperative complication.

Background: The ultrasonic surgical aspirator is widely used in intracranial tumor resection as this instrument is considered safe. The advantage of an ultrasonic surgical aspirator is that it does not damage vessels or nerves close to the tumor. Therefore, limited information exists regarding intraoperative arterial injury by the ultrasonic surgical aspirator. Case Description: We report two cases. The first case was a 30-year-old woman who underwent surgery for a recurrent craniopharyngioma, and the second was a 50-year-old man who underwent surgery for a meningioma. A craniopharyngioma encased the basilar artery in the former case, and the superior cerebellar artery was encased by a meningioma in the latter. An ultrasonic surgical aspirator was used to resect the tumors in two cases. During surgery, the arteries involved in the tumors were unintentionally injured using an ultrasonic surgical aspirator. Intraoperative hemostasis was achieved for the bleeding from the injured arteries. However, postoperative digital cerebral angiography revealed pseudoaneurysms in the injured arteries. A subarachnoid hemorrhage occurred in the first case. The pseudoaneurysms were managed using endovascular embolization. Conclusion: Intraoperative arterial injury can occur with the application of an ultrasonic surgical aspirator. Neurosurgeons should be cautious when using ultrasonic surgical aspirators to avoid damaging the arteries involved with the tumor.

Pleomorphic xanthoastrocytoma with NTRK fusion presenting as spontaneous intracranial hemorrhage-case report and literature review.

Background: Pleomorphic xanthoastrocytoma (PXA) is a rare brain tumor that accounts for <1% of all gliomas. An in-depth understanding of PXA's molecular makeup remains a work in progress due to its limited numbers globally. Separately, spontaneous intracranial hemorrhage (pICH) is an uncommon but potentially devastating emergency in young children, often caused by vascular malformations or underlying hematological conditions. We describe an interesting case of a toddler who presented with pICH, later found to have a PXA as the underlying cause of hemorrhage. Further molecular interrogation of the tumor revealed a neurotrophic tyrosine receptor kinase (NTRK) gene fusion and CDKN2A deletion more commonly seen in infantile high-grade gliomas. The unusual clinicopathological features of this case are discussed in corroboration with published literature. Case presentation: A previously well 2-year-old male presented with acute drowsiness and symptoms of increased intracranial pressure secondary to a large right frontoparietal intracerebral hematoma. He underwent an emergency craniotomy and partial evacuation of the hematoma for lifesaving measures. Follow-up neuroimaging reported a likely right intra-axial tumor with hemorrhagic components. Histology confirmed the tumor to be a PXA (WHO 2). Additional molecular investigations showed it was negative for BRAFV600E mutation but was positive for CDKN2A homozygous deletion and a unique neurotrophic tyrosine receptor kinase (NTRK) gene fusion. The patient subsequently underwent second-stage surgery to proceed with maximal safe resection of the remnant tumor, followed by the commencement of adjuvant chemotherapy. Conclusion: To date, there are very few pediatric cases of PXA that present with spontaneous pICH and whose tumors have undergone thorough molecular testing. Our patient's journey highlights the role of a dedicated multidisciplinary neuro-oncology team to guide optimal treatment.

Association between Postoperative Decrease of Albumin and Outcomes in Patients Undergoing Craniotomy for Brain Tumors.

BACKGROUND: Serum albumin reflects nutritional status and is associated with postoperative complications and mortality. Delta albumin (ΔAlb), defined as the difference between preoperative and lowest postoperative levels, could predict complications and mortality, even with post-op levels above 30 g/L prompting albumin infusions. This study aimed to assess how ΔAlb relates to outcomes in craniotomy patients with brain tumors. METHODS: This retrospective study screened patients diagnosed with a brain tumor who underwent cerebral surgery from a single Chinese hospital between December 2010 and April 2021. Patients were divided into four groups based on their ΔAlb levels: <5 g/L (normal), 5-9.9 g/L (mild ΔAlb), 10-14.9 g/L (moderate ΔAlb), and ≥15 g/L (severe ΔAlb). The primary outcome was postoperative 30-day mortality. RESULTS: Among the 9660 patients undergoing craniotomy for brain tumors, the median ΔAlb level after craniotomy was 7.3 g/L. ΔAlb was associated with increased postoperative 30-day mortality; Odds ratios (OR) for mild, moderate, and severe ΔAlb were 1.93(95% CI, 1.17-3.18,P=0.01), 2.21(95% CI, 1.28-3.79,P=0.004), and 7.26(95% CI, 4.19-12.58,P<0.01), respectively. Significantly, ΔAlb >5g/L was found to have a strong association with a higher risk of mortality, even when the nadir Alb remained greater than 30 g/L (OR, 1.84; 95% CI, 1.13- 3.00, P=0.014). CONCLUSIONS: Among patients undergoing craniotomy for brain tumor resection, a mild degree of ΔAlb was associated with increased 30-day mortality, even if the nadir Alb remained greater than 30 g/L. Moreover, ΔAlb was associated with postoperative complications and longer lengths of stay.

Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey.

Radiologists encounter significant challenges when segmenting and determining brain tumors in patients because this information assists in treatment planning. The utilization of artificial intelligence (AI), especially deep learning (DL), has emerged as a useful tool in healthcare, aiding radiologists in their diagnostic processes. This empowers radiologists to understand the biology of tumors better and provide personalized care to patients with brain tumors. The segmentation of brain tumors using multi-modal magnetic resonance imaging (MRI) images has received considerable attention. In this survey, we first discuss multi-modal and available magnetic resonance imaging modalities and their properties. Subsequently, we discuss the most recent DL-based models for brain tumor segmentation using multi-modal MRI. We divide this section into three parts based on the architecture: the first is for models that use the backbone of convolutional neural networks (CNN), the second is for vision transformer-based models, and the third is for hybrid models that use both convolutional neural networks and transformer in the architecture. In addition, in-depth statistical analysis is performed of the recent publication, frequently used datasets, and evaluation metrics for segmentation tasks. Finally, open research challenges are identified and suggested promising future directions for brain tumor segmentation to improve diagnostic accuracy and treatment outcomes for patients with brain tumors. This aligns with public health goals to use health technologies for better healthcare delivery and population health management.

Unraveling contributions to the Z-spectrum signal at 3.5 ppm of human brain tumors.

PURPOSE: To evaluate the influence of the confounding factors, direct water saturation (DWS), and magnetization transfer contrast (MTC) effects on measured Z-spectra and amide proton transfer (APT) contrast in brain tumors. METHODS: High-grade glioma patients were scanned using an RF saturation-encoded 3D MR fingerprinting (MRF) sequence at 3 T. For MRF reconstruction, a recurrent neural network was designed to learn free water and semisolid macromolecule parameter mappings of the underlying multiple tissue properties from saturation-transfer MRF signals. The DWS spectra and MTC spectra were synthesized by solving Bloch-McConnell equations and evaluated in brain tumors. RESULTS: The dominant contribution to the saturation effect at 3.5 ppm was from DWS and MTC effects, but 25%-33% of the saturated signal in the gadolinium-enhancing tumor (13%-20% for normal tissue) was due to the APT effect. The APT# signal of the gadolinium-enhancing tumor was significantly higher than that of the normal-appearing white matter (10.1% vs. 8.3% at 1 µT and 11.2% vs. 7.8% at 1.5 µT). CONCLUSION: The RF saturation-encoded MRF allowed us to separate contributions to the saturation signal at 3.5 ppm in the Z-spectrum. Although free water and semisolid MTC are the main contributors, significant APT contrast between tumor and normal tissues was observed.

Identification and validation of miRNA-target genes network in pediatric brain tumors.

Alterations in miRNA levels have been observed in various types of cancer, impacting numerous cellular processes and increasing their potential usefulness in combination therapies also in brain tumors. Recent advances in understanding the genetics and epigenetics of brain tumours point to new aberrations and associations, making it essential to continually update knowledge and classification. Here we conducted molecular analysis of 123 samples of childhood brain tumors (pilocytic astrocytoma, medulloblastoma, ependymoma), focusing on identification of genes that could potentially be regulated by crucial representatives of OncomiR-1: miR-17-5p and miR-20a-5p. On the basis of microarray gene expression analysis and qRTPCR profiling, we selected six (WEE1, CCND1, VEGFA, PTPRO, TP53INP1, BCL2L11) the most promising target genes for further experiments. The WEE1, CCND1, PTPRO, TP53INP1 genes showed increased expression levels in all tested entities with the lowest increase in the pilocytic astrocytoma compared to the ependymoma and medulloblastoma. The obtained results indicate a correlation between gene expression and the WHO grade and subtype. Furthermore, our analysis showed that the integration between genomic and epigenetic pathways should now point the way to further molecular research.

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

Cervical Lymph Node Metastasis From Medulloblastoma in a Young Adult: Case Report and Literature Review.

Medulloblastoma, an embryonal tumor located in the posterior fossa of the brain, originates from the neuro-epidermal layer of the cerebellum. It is the most prevalent malignant tumor in children, while it is rare in adults and predominantly affects males. Multimodal therapeutic interventions, such as surgery, radiotherapy, and chemotherapy, have substantially enhanced the prognosis of this condition. Extraneural metastases are infrequent. We present a case of medulloblastoma relapse with nodal metastasis in a 28-year-old adult.

Enhancing brain tumor detection in MRI with a rotation invariant Vision Transformer.

Background: The Rotation Invariant Vision Transformer (RViT) is a novel deep learning model tailored for brain tumor classification using MRI scans. Methods: RViT incorporates rotated patch embeddings to enhance the accuracy of brain tumor identification. Results: Evaluation on the Brain Tumor MRI Dataset from Kaggle demonstrates RViT's superior performance with sensitivity (1.0), specificity (0.975), F1-score (0.984), Matthew's Correlation Coefficient (MCC) (0.972), and an overall accuracy of 0.986. Conclusion: RViT outperforms the standard Vision Transformer model and several existing techniques, highlighting its efficacy in medical imaging. The study confirms that integrating rotational patch embeddings improves the model's capability to handle diverse orientations, a common challenge in tumor imaging. The specialized architecture and rotational invariance approach of RViT have the potential to enhance current methodologies for brain tumor detection and extend to other complex imaging tasks.

Efficacy of perampanel by etiology in Japanese patients with epilepsy-subpopulation analysis of a prospective post-marketing observational study.

OBJECTIVE: To examine the efficacy and safety of perampanel (PER) in patients with post-stroke epilepsy (PSE), brain tumor-related epilepsy (BTRE), and post-traumatic epilepsy (PTE) using Japanese real-world data. METHODS: The prospective post-marketing observational study included patients with focal seizures with or without focal to bilateral tonic-clonic seizures who received PER combination therapy. The observation period was 24 or 52 weeks after the initial PER administration. The safety and efficacy analysis included 3716 and 3272 patients, respectively. This post hoc analysis examined responder rate (50% reduction in seizure frequency), seizure-free rate (proportion of patients who achieved seizure-free), and safety in patients included in the post-marketing study who had PSE, BTRE, and PTE in the 4 weeks prior to the last observation. RESULTS: Overall, 402, 272, and 186 patients were included in the PSE, BTRE, and PTE subpopulations, and 2867 controls in the "Other" population (etiologies other than PSE, BTRE, or PTE). Mean modal dose (the most frequently administered dose) values at 52 weeks were 3.38, 3.36, 3.64, and 4.04 mg/day for PSE, BTRE, PTE, and "Other," respectively; PER retention rates were 56.2%, 54.0%, 52.6%, and 59.7%, respectively. Responder rates (% [95% confidence interval]) were 82% (76.3%-86.5%), 78% (70.8%-83.7%), 67% (56.8%-75.6%), and 50% (47.9%-52.7%) for PSE, BTRE, PTE, and "Other," respectively, and seizure-free rates were 71% (64.5%-76.5%), 62% (54.1%-69.0%), 50% (40.6%-60.4%), and 28% (25.8%-30.1%), respectively. Adverse drug reactions tended to occur less frequently in the PSE (14.7%), BTRE (16.5%), and PTE (16.7%) subpopulations than in the "Other" population (26.3%). SIGNIFICANCE: In real-world clinical conditions, efficacy and tolerability for PER combination therapy were observed at low PER doses for the PSE, BTRE, and PTE subpopulations. PLAIN LANGUAGE SUMMARY: To find out how well the medication perampanel works and whether it is safe for people who have epilepsy after having had a stroke, brain tumor, or head injury, we used information from real-life medical situations in Japan. We looked at the data of about 3700 Japanese patients with epilepsy who were treated with perampanel. We found that perampanel was used at lower doses and better at controlling seizures, and had fewer side effects for patients with epilepsy caused by these etiologies than the control group.

Demystifying the potential of lipid-based nanocarriers in targeting brain malignancies.

Drug targeting for brain malignancies is restricted due to the presence of the blood-brain barrier (BBB) and blood-brain tumor barrier (BBTB), which act as barriers between the blood and brain parenchyma. Certainly, the limited therapeutic options for brain malignancies have made notable progress with enhanced biological understanding and innovative approaches, such as targeted therapies and immunotherapies. These advancements significantly contribute to improving patient prognoses and represent a promising shift in the landscape of brain malignancy treatments. A more comprehensive understanding of the histology and pathogenesis of brain malignancies is urgently needed. Continued research focused on unraveling the intricacies of brain malignancy biology holds the key to developing innovative and tailored therapies that can improve patient outcomes. Lipid nanocarriers are highly effective drug delivery systems that significantly improve their solubility, bioavailability, and stability while also minimizing unwanted side effects. Surface-modified lipid nanocarriers (liposomes, niosomes, solid lipid nanoparticles, nanostructured lipid carriers, lipid nanocapsules, lipid-polymer hybrid nanocarriers, lipoproteins, and lipoplexes) are employed to improve BBB penetration and uptake through various mechanisms. This systematic review illuminates and covers various topics related to brain malignancies. It explores the different methods of drug delivery used in treating brain malignancies and delves into the benefits, limitations, and types of brain-targeted lipid-based nanocarriers. Additionally, this review discusses ongoing clinical trials and patents related to brain malignancy therapies and provides a glance into future perspectives for treating this condition.

Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization.

Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal brain tissue these days. It is a difficult undertaking for radiologists to diagnose and classify the tumor from several pictures. This work develops an intelligent method for accurately identifying brain tumors. This research investigates the identification of brain tumor types from MRI data using convolutional neural networks and optimization strategies. Two novel approaches are presented: the first is a novel segmentation technique based on firefly optimization (FFO) that assesses segmentation quality based on many parameters, and the other is a combination of two types of convolutional neural networks to categorize tumor traits and identify the kind of tumor. These upgrades are intended to raise the general efficacy of the MRI scan technique and increase identification accuracy. Using MRI scans from BBRATS2018, the testing is carried out, and the suggested approach has shown improved performance with an average accuracy of 98.6%.

Relationships between cognitive flexibility performance and adaptive behavior outcomes in survivors of pediatric brain tumor.

Objective: Survivors of pediatric brain tumors are at increased risk of executive function (EF) and adaptive behavior difficulties. While previous research suggests that executive dysfunction impacts suboptimal adaptive outcomes, the specific elements of EF influencing this relationship remain unexplored. This study examines the relationship between cognitive flexibility and adaptive behavior in survivors compared to healthy controls. Methods: 86 survivors (Mage(SD)=23.41(4.24), 44 females) and 86 controls (Mage(SD)=23.09(4.50), 44 females) completed the Delis-Kaplan Executive Function System Trail Making Test (TMT) and Verbal Fluency Test (VFT). The Letter-Number Sequencing (LNS) and Category Switching (CS) conditions were isolated as measures of cognitive flexibility. Informants provided responses to obtain adaptive behavior ratings using the Scales of Independent Behavior-Revised (SIB-R). Linear regressions explored relationships between cognitive flexibility and SIB-R scores in survivors compared to controls. Results: For both TMT and VFT, the relationship between cognitive flexibility and adaptive behavior was significantly different between survivors and controls for SIB-R scores in Social Communication, Community Living, and Personal Living Skills (p<.0125). Survivors' better LNS performance predicted greater SIB-R scores across the same 3 domains (all p= <.001, r2semipartial=.08). Similarly, survivors' better CS performance predicted greater SIB-R scores across the same 3 domains (p = 0.002 to .02, r2semipartial =.03 to .04). No significant relationships were found in controls (all p >.05). After adjusting for working memory and inhibitory control, most relationships remained significant in survivors (p= <.001 to .046, r2semipartial=.02 to .08). Conclusion: These findings reveal a robust, positive relationship between cognitive flexibility performance and adaptive behaviors specific to survivors.

Endoscopic and exoscopic surgery for brain tumors.

Nerves and blood vessels must be protected during brain tumor surgery, which has traditionally relied on microscopes. In the 2000s, endoscopes and related equipment were developed for neurosurgery. In this review, we aim to outline the role of endoscopes in brain tumor surgery and discuss the emerging use of exoscopes. The primary use of endoscopes in brain tumor surgery is in endoscopic endonasal surgery for pituitary tumors. By using the space within the sphenoid sinus, surgeons can insert an endoscope and instruments such as forceps or scissors through the nose to access and remove the tumor. Compared to microscopes, endoscopes can get closer to tumors, nerves, and blood vessels. They enable wide-angle observation of the skull base, making them valuable for skull base tumors as well as pituitary tumors. Endoscopes are also used in cases where a brain tumor is associated with hydrocephalus, allowing surgeons to correct obstructive hydrocephalus and perform tumor biopsies simultaneously. Exoscopy, a newer technique introduced in recent years, involves surgeons wearing special glasses and removing the tumor while viewing a three-dimensional monitor. This approach reduces surgeon fatigue and allows for more natural positioning during lengthy brain tumor surgeries. Future brain tumor surgeries will likely involve robotic surgery, which is already used for other organs. This is expected to make brain tumor removal safer and more accurate.

The Impact of Social Determinants on Receipt of Adjuvant Radiation Nationally Following Atypical Meningioma Surgery.

PURPOSE: We aimed to identify socioeconomic gaps in the administration of adjuvant radiotherapy for patients with atypical meningioma and secondarily to determine differences in survival between patients receiving radiation and those not receiving radiation at 12 and 60 months. METHODS: The National Cancer Database was queried for patients receiving atypical meningioma surgery between 2004 and 2019. Statistical analyses were performed to assess the association between receipt of adjuvant radiation and social determinants. Secondarily, KM curves were used to compare overall patient survival between those that received radiation and those that did not. RESULTS: Adjuvant radiation was less likely to be administered to patients over 65 (95% CI =0.53- 22 0.77) and more likely to be administered to males (95% CI =1.07-1.38). Compared to the Southern USA, patients were more likely to receive radiotherapy in the Northeastern (95% CI 24 =1.40-2.05), Midwestern (95% CI =1.06-1.54), and Western parts of the USA (95% 25 CI =1.31-2.00). Patients residing furthest from their facility were less likely to receive radiation (95% CI =0.65-0.98). Insured patients were more likely to receive radiation (p = 0.048) than uninsured patients. On multivariate analysis, no differences were found between racial groups regarding adjuvant radiation. For patients unstratified, radiation was shown to improve survival at 12 and 60 months. CONCLUSION: Disparities exist in the administration of adjuvant radiotherapy for atypical meningioma. Patients over 65, women, those residing in the southern USA, those living further from their facilities and uninsured patients are less likely to receive radiation than their counterparts.

A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification.

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.

Helmet Radio Frequency Phased Array Applicators Enhance Thermal Magnetic Resonance of Brain Tumors.

Thermal Magnetic Resonance (ThermalMR) integrates Magnetic Resonance Imaging (MRI) diagnostics and targeted radio-frequency (RF) heating in a single theranostic device. The requirements for MRI (magnetic field) and targeted RF heating (electric field) govern the design of ThermalMR applicators. We hypothesize that helmet RF applicators (HPA) improve the efficacy of ThermalMR of brain tumors versus an annular phased RF array (APA). An HPA was designed using eight broadband self-grounded bow-tie (SGBT) antennae plus two SGBTs placed on top of the head. An APA of 10 equally spaced SGBTs was used as a reference. Electromagnetic field (EMF) simulations were performed for a test object (phantom) and a human head model. For a clinical scenario, the head model was modified with a tumor volume obtained from a patient with glioblastoma multiforme. To assess performance, we introduced multi-target evaluation (MTE) to ensure whole-brain slice accessibility. We implemented time multiplexed vector field shaping to optimize RF excitation. Our EMF and temperature simulations demonstrate that the HPA improves performance criteria critical to MRI and enhances targeted RF and temperature focusing versus the APA. Our findings are a foundation for the experimental implementation and application of a HPA en route to ThermalMR of brain tumors.